Date of this Version
Journal of Machine Learning Research
Chain graphs present a broad class of graphical models for description of conditional independence structures, including both Markov networks and Bayesian networks as special cases. In this paper, we propose a computationally feasible method for the structural learning of chain graphs based on the idea of decomposing the learning problem into a set of smaller scale problems on its decomposed subgraphs. The decomposition requires conditional independencies but does not require the separators to be complete subgraphs. Algorithms for both skeleton recovery and complex arrow orientation are presented. Simulations under a variety of settings demonstrate the competitive performance of our method, especially when the underlying graph is sparse.
chain graph, conditional independence, decomposition, graphical model, structural learning
Ma, Z., Xie, X., & Geng, Z. (2008). Structural Learning of Chain Graphs via Decomposition. Journal of Machine Learning Research, 9 2847-2880. Retrieved from https://repository.upenn.edu/statistics_papers/220
Date Posted: 27 November 2017
This document has been peer reviewed.